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Using Data to Generate Evidence Which Drives Value
Session SC3, February 11, 2019
James E. Tcheng, MD
Professor of Medicine, Professor of Informatics
Duke University Health System, Durham, NC
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James E. Tcheng, MD
Fees for non-CME services received directly from a commercial
interest or their agents: GE Healthcare
Ownership interest (stocks, stock options or other ownership
interest excluding diversified mutual funds): International
Guidelines Central, LivMor
Data Safety Monitoring Board: AstraZeneca
Relationships with Industry
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Establish the strategic importance of using data captured across
the point of use and its ability to solve the cost/quality equation
Identify ways to disseminate data so that it guides and informs
clinical practice
Review the technologies needed to create, maintain, and support
robust data capture
Examine the challenge and opportunities with data governance
across enterprises
Learning Objectives
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At the intersection of cost, quality and outcomes:
Aligned hospital administration, supply chain, physicians
Data, data, data
Given the strategic importance of data
Challenge 1: high quality data
Challenge 2: workflow integrated data capture at point of use
Complex systems theory
Data management logistics
What are the goals?
What data is needed?
Why not wait for “interoperability” to solve this?
What is best practice for capture of high quality data?
Who are external drivers?
FDA (UDI), NESTcc
Topics
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“Chronic diseases can be studied, but
not by the methods of the past. If one
wishes to create useful data …
computer technology must be
exploited.”
Eugene Stead, MD (ca. 1965)
Data and Health Research
A New Concept in Medicine?
Led to the concept of the “computerized
textbook of medicine
Foundation of the Duke Databank for
Cardiovascular Diseases (DCRI)
Spurred a generation of clinical and
quantitative researchers
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Is Healthcare Changing for the Better
Envisioned Reality
EHR “Meaningful Use EHR meaningless burden
Usability and productivity Death by clicking
Patient engagement AVS drivel
Effective clinical care CDS trivial pursuit
Population health Resource consumption focus
Bending healthcare cost curve Cost control and penalties
Better provider work life NOT!
Torrent of real-world data Puddles of document exchange
Big (clinical) data analytics Small transactions data
Leveraged RCTs via registries 20
th
century paradigms
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Aspiration
If you don’t know where you are going,
chances are you will end up somewhere else.
Yogi Berra
If you don’t know where you are going,
any road will take you there.
Lewis Carroll
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Is Healthcare Changing for the Better
What Should the Common Denominator Be?
Clinical documentation
Registry reporting
Quality and performance
Supply chain
Device safety, surveillance
Clinical research
RWD RWE
Computational constructs:
CDS, AI, machine learning, etc.
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The Importance of Data Quality

 
D. Fridsma, 2018
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Swivel Chair Interoperability
W. Rishel
Documents in
Clinical Systems
Registry Data Entry
(and any other
requirement for data)
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American College of Cardiology -
National Cardiovascular Data Registry:
CathPCI Operational Environment
>90% US PCI procedures, process and quality focus
Relies on clinical documentation and “swivel chair”
interoperability (i.e., little structured reporting)
NCDR data dictionary: clinical / operational focus, not data
standards (primarily name of concept, allowed values)
No authoritative process to follow for developing data
elements as HIT standards (& no single repository)
No consistent instrument for technical consumers (HIT
vendors, db developers): data elements, data capture
instruments, workflow engineering
$10 million+ book of business $500 million+ site
expenditure (2000 sites, 2+ FTEs per site)
Complex systems
theory is a combination
of linear, nonlinear,
dynamic, self-
organizing, evolutionary,
diverse, and uniform
relationships, which
makes for a messy
universe …
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Key Healthcare Targets
Clinical care: data as a medical partner
X Clinical documentation focused on care (not billing)
X Engineered data capture via care processes
X Outcomes-based (not just systems / processes)
X Incorporate patient-reported information, f/u
X Clinical decision support, pre-authorization support,
reduction in variability, revenue recovery
Research: changing the RCT paradigm
X Observational / CE studies, pragmatic trials
Regulatory: changing the evidence paradigm
X RWD RWE (not just adverse events / recalls)
X Pharmacovigilance, device surveillance
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Excellence & Mediocrity
“A society which scorns
excellence in plumbing simply
because it is plumbing, but
rewards mediocre philosophy
simply because it is philosophy,
will soon become a society in
which neither its pipes nor its
theories will hold water.”
John W. Gardner (1961)
HEW Secretary under Lyndon Johnson
Engineer of the Great Society (Medicare, PBS)
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Exchange, Use, and Reuse of Data
Requires Shared Data Definitions,
Including Semantics
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What is Required to Share Data?
What: conceptual clinical definitions (vocabulary)
When, where: interfaces (forms) to capture specific data
at point of care by individuals closest to the data
How:
physical layer (data capture and storage, native
data interoperability)
communication layer (syntactic, semantic
interoperability)
common / standard format (data modeling)
bindings to controlled terminologies (SNOMED-
CT, LOINC, RxNorm)
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Data Destinations: Multiple Masters
Clinical care, documentation
Payers
Quality assessment, registries, CRNs
Patients
Health system / population health
Research
FDA, surveillance reporting
Federal, state reporting programs
Oh yes … clinicians
Recipients
Producers
… who are time-challenged, short-staffed, over-burdened …
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HIT /
EHR
(POC
form)
Discrete
Data
(CDEs)
Structured
Documentation
DQR
Credible
Data
Analysis,
Measures
Benchmark
Registries
Active
Quality
Improvement
Cycle
Duke Heart Center - Dataflow End State
Heart
Data
Mart
Research
Build infrastructure
Use the data
Near Real Time Clean Up
Structured
Reporting
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Concurrent Data Acquisition Model
and Data Flow Into Registries
Clinical data collected during case
Registry data
mapping rules
Medical
History event
Cath/PVI/EP/CT
Surgery report
Registry
submission
Type I diabetes
Type II diabetes
Type I Diabetes If Type I = Yes
then Diabetes
= Yes
Diabetes = Yes
Unidirectional carry
forward (CF)
with CF button trigger N+1
Unidirectional CF
with automated CF, plus
“Lock/Unlock” data, plus ad
hoc CF button trigger
Cath Lab / Surgery Staff
Registry Nurse
Frequent / real time, accurate, missingness reports,
real time metrics, real time risk calculation
Data quality
Performance improvement
X
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What is Structured Reporting?
Explicit clinical data captured by the person closest to that
data, integrated into clinical workflow (e.g. MA, tech, RN, pt)
Informatics formalisms: universal, well-defined common data
elements; data model that parallels (i.e., is representational
of) clinical care model
Data compiled by the computer to produce majority of report
content; MD validates the data, contributes only cognitive
assessment and recommendations
Output: the structured report
ROI: data quality /quantity, redundancy / repetition, time to
final reports, FTE requirements augmented knowledge,
revenue recovery, financial gains
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How Is Structured Reporting Done?
Engineered, best-practice workflows
Just in time, context specific, high usability,
point of care data capture
Data persistence
Lots of business rules
Optimized IT form factors
In other words …
Command of who does what when, where,
and how
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Critical
Step
Device (UDI) + Clinical Data Integration
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Usage DFT
Pt data
Accession #
UDI (DI + PI)
Serial, lot #
Expiration
Procedure
data
Pyxis
Procedure
Stations
BD CCE
Care
Coordination
Engine
Lumedx
Procedure
Documentation
Epic
Radiant
EHR
SAP Replenishment
JIT Ordering
SAP Item Master
Orders ORM
Pt data
Accession, case #
Procedure data
Items data (ASCII)
Inventory replenishment data
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What Does UDI Enable?
Standardized device description procedure report
no more MD recall, transcription errors
Inventory management (lower PAR levels >$1m
savings / yr - Mercy Cath Lab)
Device use attribution (e.g., waste, failure to deploy)
Consignment device management
UDI to the EHR (exported to the UDI device table)
Device explants (e.g., CIED) closing the loop
Administrative reporting (e.g., device usage reports)
Adverse event reporting (e.g., FDA MedWatch)
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How Important is it to Identify an Implant?
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Consumers - Decisions Within Days
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Registry Assessment of Peripheral
Interventional Devices (RAPID)
The MDEpiNet RAPID project is designed to advance
the foundational elements of the approach for the
evaluation of medical devices used to treat and
manage peripheral artery disease.
RAPID is an archetype of the total product
lifecycle (TPLC) ecosystem.
It is one of a series of projects initiated to advance
and demonstrate the interoperable flow of data
across electronic health information systems.
Is fundamental to the basis of the development of the National
Evaluation System for Health Technology (NEST).
A demonstration project of MDIC/NESTcc, a public-private
partnership.
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GUDID/Informatics Workgroup Phase III
1.Objective
a) To improve UDI capture/utilization and broaden its impact
b) Demonstration Project
Use findings and partnerships built in Phase I and Phase II to
form a partner-based quality improvement study of UDI workflow
Develop process to capture RWE from selected data partners for
worldwide regulatory support of device TPLC
2.Methodology
Clarifying structured data device parameters to be assigned by
manufacturers to improve quality of clinically relevant size and device
categorization in GUDID
Assessing existing workflows at NESTcc data partners who are
committed and show high level of UDI adoption maturity
UDI in RAPID:
Improve Decision Making with Better UDI Data
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Methodology (cont’d)
Assisting implementation of the core RAPID clinical data elements
(including UDI) into EHR or other point of care systems
Exploring mechanism for transfer of data into a PAD registry
Evaluating impact on data partner workflows and reductions in data
capture, data transfer, and feedback to improve value of UDI to multi-
stakeholders across the PAD lifecycle
Expected NEST impact: facilitate development of UDI workflow processes
in NEST partners that could be leveraged by other healthcare systems, as
well as evidence generation processes that could be utilized across the
medical device industry
UDI in RAPID:
Improve Decision Making with Better UDI Data
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All segments must work in concert toward common goal:
data driving the cost, quality and outcomes equation
Manufacturers: discrete data
Supply chain: integration into clinical care
HIT: solutions enabling engineered, best practice
clinical workflows
FDA: UDI, supplemental device attributes
Clinical: structured reporting
Professional societies: leadership, coordination
Healthcare systems: vision, implementation
Healthcare: data interoperability, not documents
Contact: james.tcheng@duke.edu
Summary